Copertina

Data-Driven Simulator for Mechanical Circulatory Support with Domain Adversarial Neural Process

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Sun, Sophia, Chen, Wenyuan, Zhou, Zihao, Fereidooni, Sonia, Jortberg, Elise, Yu, Rose
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Guardian-regularized Safe Offline Reinforcement Learning for Smart Weaning of Mechanical Circulatory Devices
di: Tumay, Aysin, et al.
Pubblicazione: (2025)

Neural Point Process for Learning Spatiotemporal Event Dynamics
di: Zhou, Zihao, et al.
Pubblicazione: (2021)

Can LLMs Understand Time Series Anomalies?
di: Zhou, Zihao, et al.
Pubblicazione: (2024)

Copula Conformal Prediction for Multi-step Time Series Forecasting
di: Sun, Sophia, et al.
Pubblicazione: (2022)

Conformal Prediction for Time-series Forecasting with Change Points
di: Sun, Sophia, et al.
Pubblicazione: (2025)

Empirical Comparison of Forgetting Mechanisms for UCB-based Algorithms on a Data-Driven Simulation Platform
di: Chen, Minxin
Pubblicazione: (2025)

Steering Beyond the Support: Adversarial Training on Unsupervised Jailbroken Activation Simulation
di: Chen, Luoyu, et al.
Pubblicazione: (2026)

Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework
di: Duan, Minxuan, et al.
Pubblicazione: (2024)

Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data
di: Velioglu, Mehmet, et al.
Pubblicazione: (2024)

Robust Neural Processes for Noisy Data
di: Shapira, Chen, et al.
Pubblicazione: (2024)

Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness
di: Chen, Erh-Chung, et al.
Pubblicazione: (2024)

Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
di: Niu, Ruijia, et al.
Pubblicazione: (2024)

Data-Driven Reachability Analysis via Diffusion Models with PAC Guarantees
di: Huang, Yanliang, et al.
Pubblicazione: (2026)

FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
di: Fereidooni, Hossein, et al.
Pubblicazione: (2023)

Zero-Shot Machine Unlearning with Proxy Adversarial Data Generation
di: Chen, Huiqiang, et al.
Pubblicazione: (2025)

OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection
di: Zheng, Lecheng, et al.
Pubblicazione: (2026)

Data-Driven Open-Loop Simulation for Digital-Twin Operator Decision Support in Wastewater Treatment
di: Simethy, Gary, et al.
Pubblicazione: (2026)

AsymKV: Enabling 1-Bit Quantization of KV Cache with Layer-Wise Asymmetric Quantization Configurations
di: Tao, Qian, et al.
Pubblicazione: (2024)

Adversarial Learning for Neural PDE Solvers with Sparse Data
di: Gong, Yunpeng, et al.
Pubblicazione: (2024)

Domain Adversarial Active Learning for Domain Generalization Classification
di: Chen, Jianting, et al.
Pubblicazione: (2024)

Robust Simulation-Based Inference under Missing Data via Neural Processes
di: Verma, Yogesh, et al.
Pubblicazione: (2025)

Adversarial Reweighting with $α$-Power Maximization for Domain Adaptation
di: Gu, Xiang, et al.
Pubblicazione: (2024)

Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical Sciences
di: Li, Sichao, et al.
Pubblicazione: (2024)

Data-Driven Adversarial Online Control for Unknown Linear Systems
di: Liu, Zishun, et al.
Pubblicazione: (2023)

Neural Conditional Simulation for Complex Spatial Processes
di: Walchessen, Julia, et al.
Pubblicazione: (2025)

A Data-Driven Multi-Objective Approach for Predicting Mechanical Performance, Flowability, and Porosity in Ultra-High-Performance Concrete (UHPC)
di: Chakma, Jagaran, et al.
Pubblicazione: (2025)

Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data
di: Qi, Yijiashun, et al.
Pubblicazione: (2025)

Online Adversarial Knowledge Distillation for Graph Neural Networks
di: Wang, Can, et al.
Pubblicazione: (2021)

PAC-Bayesian Adversarially Robust Generalization Bounds for Graph Neural Network
di: Sun, Tan, et al.
Pubblicazione: (2024)

Adversarial Attacks on Fairness of Graph Neural Networks
di: Zhang, Binchi, et al.
Pubblicazione: (2023)

From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation
di: Zhao, Wenyuan, et al.
Pubblicazione: (2025)

Data-Driven Subsampling in the Presence of an Adversarial Actor
di: Jameel, Abu Shafin Mohammad Mahdee, et al.
Pubblicazione: (2024)

Diffusion Model for Data-Driven Black-Box Optimization
di: Li, Zihao, et al.
Pubblicazione: (2024)

A Data-Driven Approach to Support Clinical Renal Replacement Therapy
di: Balboni, Alice, et al.
Pubblicazione: (2026)

A Physics-Constrained Neural Differential Equation Framework for Data-Driven Snowpack Simulation
di: Charbonneau, Andrew, et al.
Pubblicazione: (2024)

FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation
di: Chen, Kun, et al.
Pubblicazione: (2024)

Extrapolability Improvement of Machine Learning-Based Evapotranspiration Models via Domain-Adversarial Neural Networks
di: Shi, Haiyang
Pubblicazione: (2024)

Protego: Detecting Adversarial Examples for Vision Transformers via Intrinsic Capabilities
di: Wu, Jialin, et al.
Pubblicazione: (2025)

Simulation-Free Training of Neural ODEs on Paired Data
di: Kim, Semin, et al.
Pubblicazione: (2024)

Unveiling AI's Blind Spots: An Oracle for In-Domain, Out-of-Domain, and Adversarial Errors
di: Han, Shuangpeng, et al.
Pubblicazione: (2024)